Météo-France

Within the Explore2 national project, a new set of bias-corrected regional climate projections sub-sampled from the EURO-CORDEX (EUR11) ensemble has been produced to describe the impact of climate change on water resources and to support impact studies over mainland France. This dataset has been specially selected to reflect the expected changes in temperature and precipitation of the complete EURO-CORDEX (EUR11) ensemble while taking those of CMIP6 into account as consistency constraint. Yet, the selection allows to obtain a smaller ensemble size to handle with. The process of GCM/RCM couples selection is fully described in the article. The dataset makes it possible to characterize and partition the various sources of uncertainty about the evolution of the climate in France, by taking into account three greenhouse gas emission scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), multiple regional climate models (allowing to dispose to 9 to 17 GCM/RCM couples depending on the emission scenario), two methods of statistical bias correction (ADAMONT and CDF-t) and continuous time series to explore internal variability.This dataset contains 10 climate variables at daily resolution, enabling the calculation of a very large number of climate impact indicators, as well as its use to drive a wide variety of hydrological models in France. Examples of climate change representations suitable for this dataset are provided for cumulative precipitation at seasonal scale. These representation methods are intended to guide potential users of this data when aiming to characterize the robustness of the changes (according to individual simulations, time horizons or climate change scenarios) and to identify contrasting scenarios across a territory. A narrative approach is also proposed to facilitate the exploration of individual projections of climate change, allowing for a more accurate consideration of inter-annual variability and extremes Four narratives were selected among the 17 GCM/RCM couples in collaboration with hydrologists which correspond to contrasting changes of temperature and precipitation in order to reflect a plurality of contrasting possible climate futures within the dispersion of the Explore2–2022 dataset.The richness of this dataset and the inclusion of the most recent regional climate simulations for France justified its use in constructing and illustrating the reference warming trajectory for climate change adaptation (TRACC) in France, backed by the 3rd National Climate Change Adaptation Plan.The Explore2 project worked to build a data-set meeting the FAIR Data Principles1 to maximize transparency, easiness and re-usability of data.

Marine heatwaves (MHWs) are intensifying with climate change, endangering ecosystems such as coral reefs. Yet their regional characteristics and drivers remain poorly understood in many parts of the Pacific. Here we provide a comprehensive assessment of MHWs in the central South Pacific and across the five archipelagos of French Polynesia (FP; representing more than 5 million km2 of maritime area, a region as vast as Europe), using sea surface temperature observations and an ocean reanalysis to investigate underlying mechanisms. MHW characteristics vary widely across the region: its northern and southern parts (the Marquesas and Austral archipelagos, respectively) experience the highest number of MHW days and the strongest cumulative intensities, especially during the warm season (November–April). In contrast, its central part (the Society, Tuamotu, and Gambier Islands) exhibits more moderate MHW characteristics. Heat budget analyses highlight the seasonally and regionally diverse mechanisms shaping MHWs. In central FP during the warm season (austral summer), most MHWs are driven by air–sea heat fluxes, while in the northern part, those driven by oceanic horizontal advection dominate. During the cold season (austral winter), more MHWs driven by horizontal advection are observed in the whole region since the thicker seasonal mixed layer reduces the proportion of MHWs driven by air–sea fluxes. El Niño–Southern Oscillation (ENSO) strongly modulates MHW occurrences: El Niño favors MHW occurrences in northeastern FP, while La Niña increases MHW occurrence in the southwest with different spatial extent depending on ENSO flavors (Central or Eastern Pacific ENSO events). This modulation arises from reduced wind-evaporation cooling with reduced wind speed, shoaled mixed layers, and enhanced horizontal heat advection, occurring primarily to the northeast of French Polynesia during El Niño and to the southwest during La Niña. These results greatly improve our understanding of MHW characteristics, dynamics and variability in this ecologically-fragile region.

This review offers a comprehensive analysis of convectively coupled equatorial waves (CCEWs) and their pivotal role in driving precipitation extremes across the Maritime Continent. It examines the current understanding of CCEWs, evaluates the performance of numerical models and forecasting techniques in predicting these phenomena, and pinpoints critical areas for improvement. The discussion centers on three key types of equatorial waves: equatorial Rossby waves, Kelvin waves, and mixed Rossby–gravity waves. By connecting scientific insights with practical forecasting applications, the review sheds light on the challenges of predicting these waves while identifying opportunities to advance both fundamental knowledge and forecasting accuracy. Designed as an educational resource, it targets operational forecasting centers, meteorologists, and researchers, aiming to enhance the prediction of extreme weather events in the region. Rainfall extremes in the Maritime Continent are among the most intense on Earth, posing major challenges for both society and weather forecasting. This review highlights the crucial role of convectively coupled equatorial waves—large-scale tropical weather systems—in triggering such events. Through both their direct influence and interactions with the diurnal rainfall cycle and other weather systems, equatorial waves amplify precipitation and create favorable conditions for extreme rainfall. Despite their important role in modulating precipitation extremes, they are still underrepresented in many numerical weather prediction models, limiting forecast accuracy. Improving understanding and representation of these waves offers a pathway toward better forecasts and greater resilience in one of the world’s most vulnerable and rain-prone regions.

Since 2011, massive strandings of pelagic Sargassum have become a recurrent environmental hazard across the tropical Atlantic and Caribbean archipelago, creating an urgent need for reliable short-term drift forecasts to support coastal risk management. This study evaluates key sources of uncertainty in operational Sargassum drift forecasting by analyzing the sensitivity of Lagrangian simulations to the representation of floating material and to environmental forcing fields. The analysis uses two complementary observational datasets: trajectories of four GPS-tracked Sargassum mats deployed near Puerto Rico and thirteen 24 h displacement vectors derived from sequential Sentinel-3 satellite detections across the tropical North Atlantic. Drift simulations were performed with the MOTHY model under multiple configurations, testing two material parameterizations, different atmospheric forcings, and several ocean circulation products and vertical current integration strategies. The results indicate that the best agreement with observed trajectories is obtained for partially immersed structures, highlighting the importance of balancing wind exposure and hydrodynamic drag. Sensitivity experiments further show that ocean circulation forcing dominates trajectory skill, while higher-resolution atmospheric forcing provides limited improvement under offshore conditions. Overall, the study confirms the importance of accurately representing upper-ocean transport processes and provides observational support for several operational choices implemented in the Météo-France Sargassum forecasting system.

Time-delay and source power estimation are fundamental tasks in a plethora of applications, in particular for remote sensing systems where the goal is to characterize the reflecting surface. Standard processing considers a Gaussian conditional signal model for which all the unknown parameters are assumed to be deterministic, but it may be more informative to consider a Gaussian random surface scattering, leading to an unconditional signal model. In this contribution, compact closed-form unconditional Cramér-Rao bound (CRB) expressions for delay and Gaussian source variance estimation are provided, considering a generic band-limited signal, as well as the corresponding maximum likelihood estimators (MLEs). The CRBs are expressed in terms of the signal samples, making it especially easy to use whatever base-band signal is considered. The results are validated with two representative band-limited signals to support the discussion.